First we randomly select some producers to be our evaluation group and do not use this test group until our models below are fully built. Then we take a large set of features describing content producers such as how many followers they have, how often they produce content, etc, all de-identified. It is important that each of these features need to be observed from before the experiment started. Then we take the features to predict individual level outcomes for each producer in parallel on the treatment group and the control group, and fit a content-producer-level model, \(m_{treatment}\)  and \(m_{control}\) . This modeling can be done with any off-the-shelf machine learning model, and we found that gradient boosted decision trees (GBDT's, [1]}) or neural networks ([2]}) are good choices to capture the non-linearities with high performance. Note that our two models allow us to estimate the individual treatment effect for any single producer \(i\) , by evaluating the difference
\(m_{treatment}(i)-m_{control}(i)\) 
